Resistance and resilience to Alzheimer's disease pathology are associated with reduced cortical pTau and absence of limbic-predominant age-related TDP-43 encephalopathy in a community-based cohort.

Alzheimer's disease neuropathologic change (ADNC) is defined by progressive accumulation of ß-amyloid plaques and hyperphosphorylated tau (pTau) neurofibrillary tangles across diverse regions of brain. Non-demented individuals who reach advanced age without significant ADNC are considered to be resistant to AD, while those burdened with ADNC are considered to be resilient. Understanding mechanisms underlying ADNC resistance and resilience may provide important clues to treating and/or preventing AD associated dementia. ADNC criteria for resistance and resilience are not well-defined, so we developed stringent pathologic cutoffs for non-demented subjects to eliminate cases of borderline pathology. We identified 14 resistant (85+ years old, non-demented, Braak stage ≤ III, CERAD absent) and 7 resilient (non-demented, Braak stage VI, CERAD frequent) individuals out of 684 autopsies from the Adult Changes in Thought study, a long-standing community-based cohort. We matched each resistant or resilient subject to a subject with dementia and severe ADNC (Braak stage VI, CERAD frequent) by age, sex, year of death, and post-mortem interval. We expanded the neuropathologic evaluation to include quantitative approaches to assess neuropathology and found that resilient participants had lower neocortical pTau burden despite fulfilling criteria for Braak stage VI. Moreover, limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC) was robustly associated with clinical dementia and was more prevalent in cases with high pTau burden, supporting the notion that resilience to ADNC may depend, in part, on resistance to pTDP-43 pathology. To probe for interactions between tau and TDP-43, we developed a C. elegans model of combined human (h) Tau and TDP-43 proteotoxicity, which exhibited a severe degenerative phenotype most compatible with a synergistic, rather than simply additive, interaction between hTau and hTDP-43 neurodegeneration. Pathways that underlie this synergy may present novel therapeutic targets for the prevention and treatment of AD.

APOEε4 Genotype and Hypertension Modify 8-year Cortical Thinning: Five Occasion Evidence from the Seattle Longitudinal Study.

We investigated individual differences in longitudinal trajectories of brain aging in cognitively normal healthy adults from the Seattle Longitudinal Study covering 8 years of longitudinal change (across 5 occasions) in cortical thickness in 249 midlife and older adults (52-95 years old). We aimed to understand true brain change; examine the influence of salient risk factors that modify an individual's rate of cortical thinning; and compare cross-sectional age-related differences in cortical thickness to longitudinal within-person cortical thinning. We used Multivariate Multilevel Modeling to simultaneously model dependencies among 5 lobar composites (Frontal, Parietal, Temporal, Occipital, and Cingulate [CING]) and account for the longitudinal nature of the data. Results indicate (1) all 5 lobar composites significantly atrophied across 8 years, showing nonlinear longitudinal rate of cortical thinning decelerated over time, (2) longitudinal thinning was significantly altered by hypertension and Apolipoprotein-E ε4 (APOEε4), varying by location: Frontal and CING thinned more rapidly in APOEε4 carriers. Notably, thinning of parietal and occipital cortex showed synergistic effect of combined risk factors, where individuals who were both APOEε4 carriers and hypertensive had significantly greater 8-year thinning than those with either risk factor alone or neither risk factor, (3) longitudinal thinning was 3 times greater than cross-sectional estimates of age-related differences in thickness in parietal and occipital cortices.

Time Curves: Folding Time to Visualize Patterns of Temporal Evolution in Data.

We introduce time curves as a general approach for visualizing patterns of evolution in temporal data. Examples of such patterns include slow and regular progressions, large sudden changes, and reversals to previous states. These patterns can be of interest in a range of domains, such as collaborative document editing, dynamic network analysis, and video analysis. Time curves employ the metaphor of folding a timeline visualization into itself so as to bring similar time points close to each other. This metaphor can be applied to any dataset where a similarity metric between temporal snapshots can be defined, thus it is largely datatype-agnostic. We illustrate how time curves can visually reveal informative patterns in a range of different datasets.